• Tom Van Herpe, Jos De Brabanter, Martine Beullens, Bart De Moor, and Greet Van den Berghe.
• Katholieke Universiteit Leuven, Department of Electrical Engineering (ESAT), Research Division SCD, Kasteelpark Arenberg 10, B-3001 Leuven (Heverlee), Belgium. tom.vanherpe@esat.kuleuven.be
• Crit Care. 2008 Jan 1; 12 (1): R24.

IntroductionBlood glucose (BG) control performed by intensive care unit (ICU) nurses is becoming standard practice for critically ill patients. New (semi-automated) 'BG control' algorithms (or 'insulin titration' algorithms) are under development, but these require stringent validation before they can replace the currently used algorithms. Existing methods for objectively comparing different insulin titration algorithms show weaknesses. In the current study, a new approach for appropriately assessing the adequacy of different algorithms is proposed.MethodsTwo ICU patient populations (with different baseline characteristics) were studied, both treated with a similar 'nurse-driven' insulin titration algorithm targeting BG levels of 80 to 110 mg/dl. A new method for objectively evaluating BG deviations from normoglycemia was founded on a smooth penalty function. Next, the performance of this new evaluation tool was compared with the current standard assessment methods, on an individual as well as a population basis. Finally, the impact of four selected parameters (the average BG sampling frequency, the duration of algorithm application, the severity of disease, and the type of illness) on the performance of an insulin titration algorithm was determined by multiple regression analysis.ResultsThe glycemic penalty index (GPI) was proposed as a tool for assessing the overall glycemic control behavior in ICU patients. The GPI of a patient is the average of all penalties that are individually assigned to each measured BG value based on the optimized smooth penalty function. The computation of this index returns a number between 0 (no penalty) and 100 (the highest penalty). For some patients, the assessment of the BG control behavior using the traditional standard evaluation methods was different from the evaluation with GPI. Two parameters were found to have a significant impact on GPI: the BG sampling frequency and the duration of algorithm application. A higher BG sampling frequency and a longer algorithm application duration resulted in an apparently better performance, as indicated by a lower GPI.ConclusionThe GPI is an alternative method for evaluating the performance of BG control algorithms. The blood glucose sampling frequency and the duration of algorithm application should be similar when comparing algorithms.

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